656 citations · 1.2k across the 22 of their papers we have counts for
12 papers · 1 filter
Invaria: Learning Scale and Density Invariance in Point Clouds via Next-Resolution Prediction
Chun-Peng Chang, Shaoxiang Wang, Alain Pagani +2
Modern image encoders achieve high generalization by decoupling semantic meaning from resolution, an ability yet to be fully realized in the 3D domain. We investigate the failure o…
TerraSeg: Self-Supervised Ground Segmentation for Any LiDAR
Ted Lentsch, Santiago Montiel-Marín, Holger Caesar +1
LiDAR perception is fundamental to robotics, enabling machines to understand their environment in 3D. A crucial task for LiDAR-based scene understanding and navigation is ground se…
CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning
Marcell Kegl, Andras Palffy, Csaba Benedek +1
In this paper, we address extrinsic calibration for camera, lidar, and 4D radar sensors. Accurate extrinsic calibration of radar remains a challenge due to the sparsity of its data…
UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes
Ted Lentsch, Holger Caesar, Dariu M. Gavrila
Unsupervised 3D object detection methods have emerged to leverage vast amounts of data without requiring manual labels for training. Recent approaches rely on dynamic objects for l…
Multimodal Object Query Initialization for 3D Object Detection
Mathijs R. van Geerenstein, Felicia Ruppel, Klaus Dietmayer +1
3D object detection models that exploit both LiDAR and camera sensor features are top performers in large-scale autonomous driving benchmarks. A transformer is a popular network ar…
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
Fangqiang Ding, Andras Palffy, Dariu M. Gavrila +1
This work proposes a novel approach to 4D radar-based scene flow estimation via cross-modal learning. Our approach is motivated by the co-located sensing redundancy in modern auton…
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?
Thomas M. Hehn, Julian F. P. Kooij, Dariu M. Gavrila
Various state-of-the-art self-supervised visual representation learning approaches take advantage of data from multiple sensors by aligning the feature representations across views…
Structural Knowledge Distillation for Object Detection
Philip de Rijk, Lukas Schneider, Marius Cordts +1
Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student. KD has pr…
Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data
Christoph B. Rist, David Emmerichs, Markus Enzweiler +1
Semantic scene completion is the task of jointly estimating 3D geometry and semantics of objects and surfaces within a given extent. This is a particularly challenging task on real…
CNN based Road User Detection using the 3D Radar Cube
Andras Palffy, Jiaao Dong, Julian F. P. Kooij +1
This letter presents a novel radar based, single-frame, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The…
Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery
Ries Uittenbogaard, Clint Sebastian, Julien Vijverberg +3
The current paradigm in privacy protection in street-view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurri…
The EuroCity Persons Dataset: A Novel Benchmark for Object Detection
Markus Braun, Sebastian Krebs, Fabian Flohr +1
Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection per…